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(alphabetical order)

Information given below on the indicators currently (as of January 2017) implemented into the WUEMoCA tool refers to a set of land use and water related indciators as proposed by the project partner SIC ICWC in Tashkent (Uzbekistan). The set of indciators is supplemented by RS-driven key indicators on the status of land use as proposed by the project partner at the Remote Sensing Unit of the University of Würzburg (Germany).

Generally, those indicators are applied by water and land management organizations, regional and local authorities, and professionals in dealing with routine tasks, analyzing the state of land and water resources in the region and for planning, as well as for drafting reports.

According to the Encyclopedia of Earth (2008) [1], Coops & Bater (2009) [2], and the European Environmental Agency (EEA, 2016) [3], indicators are mainly relevant in:

• Estimating the performance of irrigated agriculture within and among the boundaries of an irrigation system (e.g., BUIS, UIS, WUA, channel command area), adminstrative devisions (e.g., provinces and districts), hydrographic units (e.g., hydromodule zones), and transboundary and riparian countries and river catchments,

• Comparing different irrigation systems irrigation systems, administrative devisions, hydrographic units, and transboundary and riparian countries and river catchments (by land use and cultivated crop types, crop productivity, water productivity, water use efficiency, and others),

• Identifying problem spots, respectively localities at risk for instance in terms of drought, continious water scarcity, and others,

• Identifying consistent trends of effective improvements and to set reasonable development goals,

• Monitoring performance of irrigated agriculture over time (e.g., multi-annually, annualy, inter-annually, and seasonally) and in space.

Basically, indciators are classified as following and help to answer (EEA, 2016):

• Descriptive indicators What is happening?

• Performance indicators Does it matter? Can targets be reached?

• Efficiency indicators Is there any improvement?

• Policy effectiveness indicators Are the measures applied working?

• Total welfare indicators On the whole, is the region (system, unit) better off?

"In terms of water accounting, the proposed system of indicators helps to have a clearer picture of water distribution in river basins, to enhance ground-based monitoring with RS products, and to improve data accuracy. With this system we can understand where the water goes and where it is used." (written note from 18th May 2017 by Dr. Anatoly Sorokin and Denis Sorokin, both SIC ICWC).

Managers and deputies from water management organizations may use this set of indicators in WUEMoCA for: (i) identifying ways to save water and to improve water productivity, (ii) assessing the effectiveness and equitability of water distribution among users, (iii) identifying additional resources (water sources), including water transfers from one zone to another, (iv) identifying trade-offs for regulation of water withdrawals from transboundary and local sources, and (v) refining pronciples (rules) of distribution to minimize (or avoid) conflicts among users.

Defintions and specifications (Table 3) on the set of indicators as used in the context of WUEMoCA (as of January 2017) are given below. They are listed in alphabetical order. If not stated differentially, definitions of indicators refer to the final report of the .... WUFMAS (Water Use and Farm Management Survey) provided by SIC ICWC. Table 5 gives information for what particular crop type(s) and at which aggregation level(s) indicators implemented are available and can be selected. It is important to note that these indicators are monitored only for the maximum irrigation extent in the ASB (Figure 1) and solely for irrigated crop types in the ASB.

 - Annual information on the variety of different crop types -

In the context of WUEMoCA, the indicator 'Crop diversity' (CD) is defined as the variety of different crop types per year (annual indicaator) within a certain aggegration level. CD is calculated according the Simpson Index of Diversity (SID) [Simpson, 1949].

The irrigated cropland diversity is calculated according to the Inverse Simpson's Diversity Index using following equation:

D' = 1 - SUM pi²          with           pi = ni_{crop} / N

Where: D' = diversity index (dimensionless) per aggregation unit, pi = Diversity variable per aggregation unit, N = total number of individuals (referring to irrigated cropland pixels) within one aggregation unit of the regular raster, and ni = total number of individuals (referring to irrigated cropland pixels) per crop type within one aggregation unit of the regular raster. {crop} is the crop type to be considered.

A schematic illustration on database and calculation of the diversity index is given in Figures 1 and 2.

Figure 1. Schematic illustration on the GIS-based calculation of the crop diversity at the aggregation level of a regular raster. Each regular raster comprises aggregation units with a pixel cell size of 5 km x 5 km. Annual information on the variety of crop types is based on land use classifciation based on MODIS data. Each single MODIS pixel (cell size of 250 m x 250 m) is assigned to a certain crop type. The diversity index is calculated for all crop type pixels in one aggregation unit for the entire regular raster. Please note that this is a synthetic example meaning that the crop types (land uses) chosen are fictive in order to visualize the procedure of calculation.

Figure 2. Schematic illustration on the exemplary calculation of the crop diversity based on teh above regular raster and MODIS land use classification according to the Inverse Simpson's Diversity Index. Please note that this is a synthetic example meaning that the crop types (land uses) chosen are fictive in order to visualize the procedure of calculation. In this example, the diversity index has been calculated in excel for the reason of demonstration. Results on the diversity index in WUEMoCA are based on GIS analysis.

The annual crop diversity indicator is based on variety of different crops within an aggregation unit. This calculation can be taken to further produce maps by deploying the Simpson Index of Diversity (SID). SID marks one of the oldest means for the assessment of diversity, first presented in 1949. It has since been commonly used in diversity studies. Originally used in biology, it displays diversity as the simple concept of the chance to draw from a population two individuals of the same species. It ranges from 1 to 1/z, where z is the number of species present in the area (Simpson, 1949). In the case of this case study, z equals the number of the different crops. For this purpose, the extended version of the index is calculated by subtracting the value from its maximum of 1 using Equation 21 according to Peet (1974):

Crop rotation

- Multi-annual information on the alternation of different crop types -

Generally, the indicator 'Crop rotation' (CR) is defined as the practice of growing a sequence of crops on a particular field through time [4, 5, 6]. CR is one of the oldest and most essential agronomical practice in a crop growing systems aiming at preserving soil quality [5,7]. Thus, CR enables for the identification of long-term spatial patterns of crop cultivation. Basically, CR can be calculated in two different ways that address two relevant questions in land use analysis. These are: (i) How many different crops have been cultivated on a certain spot (represented by a pixel) for a period of interest? and (ii) how often has the cultivation of crops on a certain spot (represented by a pixel) changed for a period of interest?

First question can be answered by considering CR defined as the multi-annual number of different crop types over time. Therefore, the GIS-based calculation of CR in WUEMoCA considers the different single rasterized land use maps (MODIS) starting from 2000 and ending in 20xx (xx indicates final year of observation) and counts how many different crop types were assigned to a certain pixel in the crop type maps (here synonym to land use maps).

Table 1 gives an example on CR defined as the number of different crop types. According to this example, the one pixel has been occupied by two different crop types (i.e., cotton and wheat) within five years, Thus the CR value is 2. Figure 3 exemplarily shows the number of different crop types per pixel for a data array of rasterized land use maps for the observation period from 2000 to 2016. The resulting CR values (framed) ranging between 2 and 4 thus indicate that the individual pixels have been cultivated with 2 to 4 different crops within the period of 17 years. Generally, the higher the CR value per pixel, the more different crop types have been cultivated over time. By considering rasterized CR maps, conclusions on the cropping pattern over time can be drawn (e.g., regions of mono-cropping or regions characterized by a sequence of dissimilar crop types grown). 

Table 1. Example on the calculation of crop rotation (CR) defined as the number of different crop types for one pixel and for a short observation period of five years.

Figure 3. Schematic illustration of the GIS-based raster calculation of the 'Crop rotation' CR (framed), here defined as the number of different crop types over time, for a data array of single rasterized land use maps for the years from 2000 to 2016. The different colors indicate the crop types assigned to the according pixels in the raster layer. Please note that this is a synthetic example meaning that the crop types (land uses) chosen are fictive in order to visualize the procedure of calculation.

Table 2. Example on the calculation of crop rotation (CR) defined as the number of changes of crop types for one pixel and for a short observation period of five years.